Review and propositions for the sliding/impact wear behavior in a contact interface

A thermal-solid-liquid complex operational environment induces structural interface developing a typical coupling sliding/impact wear behavior. It results in contact damage until systems fail, which may cause significant economic losses and catastrophic consequences. The key point of solving this problem is to reveal the coupling damage mechanism of the sliding/impact behavior in typical systems and life characterization under a complicate evolving environment. This has been a hot topic in the area of mechanical reliability. The main work in this paper can be concluded as follows. Firstly, the main industries in which the “sliding/impact behavior” takes place have been introduced. Then, existing studies on the wear mechanism and degree analysis are presented, which includes surface morphology analysis, wear debris analysis, and wear degree measurement. Meanwhile, existing problems in theoretical modeling and experiments in current research are summarized, so as to point out a bright direction for future research on wear prediction. They include interface contact modeling, mathematic coupling mechanism modeling, wear equation establishment, and wear life characterization, which can provide some new ideas for improving the existing studies on the sliding/impact wear behavior.     

机载移动端场面引导软件设计与仿真研究

为了减轻飞行员在繁忙机场场面运行的工作负荷，提升场面运行安全与效率，设计了一种基于移 动电子设备运行的场面引导软件。其采用可扩展架构和模块化功能设计，通过无线通信网络与综合场面监视系 统交联，实现场面引导功能。仿真测试表明，机载移动端场面引导软件能够为飞机在场面滑行操作提供路径引 导信息和冲突告警提示，可提升运行效率和安全性，具有一定的使用价值。     

基于类脑模型与深度神经网络的目标检测与跟踪技术研究

目标检测与跟踪技术广泛应用于交通、医疗、安保和航天等领域.目前，目标检测与跟踪技术面临目标微弱、背景复杂、目标被遮挡等挑战.同时，随着脑科学研究的不断深入，人们对人脑视觉系统的理解逐渐透彻，利用类脑计算解决复杂背景下高精度目标检测与跟踪问题成为相关领域的重要研究方向.本文结合神经工程导向的类脑模型和计算机工程导向的深度神经网络(Deep Neural Networks, DNNs)，提出多种基于类脑模型与深度神经网络的目标检测与跟踪算法，包括：基于演算侧抑制的目标检测算法，基于结构 对比度(Structure Contrast, SC)视觉注意模型的弱小目标检测算法和基于记忆机制与分层卷积特征的目标跟踪算法.实验结果表明，将类脑模型和深度神经网络应用于目标检测和跟踪领域，有利于实现复杂条件下的高精度目标检测和鲁棒性目标跟踪.     

A constitutive model coupling damage and material anisotropy for wide stress triaxiality

A constitutive model that can describe the damage evolution of anisotropic metal sheets during the complex forming processes which experience wide stress triaxiality history is essential to accurately predict the deformation and rupture behaviors of the processes. In this study, a modified Lemaitre damage criterion which couples with the anisotropic Barlat 89 yield function is established. The effects of stress triaxiality, Lode parameter and shear stress on damage accumulation are considered in the constitutive model. The model is numerically implemented and applied to fracture prediction in tensile tests with different stress triaxialities and a complex deformation process with wide stress triaxiality history. The good consistency of predictions and experiments indicates that the modified Lemaitre damage model has excellent fracture prediction ability. Finally, the accuracy of the model is analyzed and discussed.     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

基于NB-IoT技术和GA-BP神经网络的车位预测系统

围绕有效整合城市停车资源,提高现有车位存量利用率的需求,构建了一种基于NB-IoT技术的车位预测系统。该系统采用NB-IoT技术进行信息采集与传输实现车位信息的共享;考虑到车位状态信息实时变化的特性,用历史车位占用数据来建立车位预测模型,推测出未来短时内车位变化趋势。为了提高车位预测的精度,采用遗传算法(Genetic algorithm,GA)优化反向传播(Back propagtion,BP)神经网络建立GA-BP神经网络车位预测模型。以某地下停车场历史数据为例进行仿真实验,研究结果表明:车位预测模型预测值与实际值相近且趋势保持一致,能够有效准确的预测车位状态变化,具有较高的精度。     

最小数据丢失量的地月中继卫星任务调度研究

月球背面的探测器必须依靠地月中继卫星进行数据传输与通信。地月中继任务包括实时性任务和延迟容忍类任务，如数传任务。当探测器等待传输的数据量超出用户存储容量时，延迟容忍类任务会由于探测器本地存储资源不足和地月中继卫星天线资源受限而无法完成，导致任务数据丢失，所以需要设计一种合理的地月中继任务调度策略，提高地月中继卫星的资源利用率，减少数据的丢失。对地月中继卫星任务调度进行了研究，在分析地月中继卫星数传任务的特点及用户的存储限制的基础上，以最小化数据丢失量为优化目标，建立了地月中继卫星任务调度模型，并设计了一种基于离散烟花算法（DFWA）的地月中继卫星任务调度算法。仿真数据分析表明，基于离散烟花算法的地月中继卫星任务调度算法在求解结果上优于遗传算法，是一种合理、有效的调度方法。     

基于TLE的弹道系数计算方法及应用分析

针对仅使用两行要素（Two Line Element，TLE）作为数据源的应用需求，研究了基于TLE轨道衰减的弹道系数计算方法。介绍了一种常用的基于两组TLE的直接计算法，分析TLE选取间隔对结果精度的影响；提出了一种基于多组TLE的迭代计算方法，以降低异常TLE对计算结果的影响；从弹道系数计算效果、在再入预报中的应用等方面对这两种方法进行比较分析。结果表明，两种方法各有优劣，基于多组TLE的迭代计算法稳定性更高、受TLE精度的影响更小；由于数据区间更短，基于两组TLE的计算结果对短期轨道衰减特性反应得更准确，用于临近再入时的预报效果更好。     

数值模拟方法对NASA CRM模型阻力预测的影响

在复杂工程外形的数值模拟中，网格类型、规模和分布、湍流模型、数值格式等都会不同程度影响模拟结果。如何评估这些模拟方法对模拟结果的影响，并识别对模拟结果影响较显著的因素，对于CFD的发展方向有积极的借鉴指导意义。为了综合研究不同因素对商用运输机外形阻力预测的影响，以AIAA第六届阻力预测会议外形NASA CRM为研究对象，同时考虑了网格、湍流模型、无黏通量格式和体心梯度求解方法等因素对阻力预测的影响。分析中采用枚举法和正交试验设计两种方法，并使用了基于聚类分析的定性敏感性分析方法和基于Mckay主影响分析的定量方法，识别出对阻力预测影响较大的因素，这为数值模拟方法的发展指明了方向。     

A novel surrogate modeling strategy of the mechanical properties of 3D braided composites

In this paper, a surrogate-based modeling methodology is developed and presented to predict the elastic properties of three dimensional (3D) four-directional braided composites. Using this approach, the prediction process becomes feasible with only a limited number of training points. The surrogate models constructed using Finite Element (FE) method and Diffuse Approximation, reduce the computational time and cost for preparing experimental samples. In the FE model, multiscale method is applied to couple the computations of elastic properties at microscale and mesoscale. Subsequently, a parametric study is performed to analyze the effects of the three design parameters on the elastic properties. Satisfactory results are obtained via the surrogate-based modeling predictions, which are compared with the experimental measurements. Moreover, the predictions obtained from surrogate models concur well with the FE predictions. This study orients a new direction for predicting the mechanical properties based on surrogate models which can effectively reduce the sample preparation cost and computational efforts.